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Modelling phenology using ordered categorical generalized additive models
David L Miller
https://arxiv.org/abs/2508.07789 https://arxiv.org/pdf/2508.07789

Modelling phenology using ordered categorical generalized additive models
One form of data collected in ecology is phenological, describing the timing of life stages. It can be tempting to analyze such data using a continuous distribution or to model individual transitions via probit/logit models. Such simplifications can lead to incorrect inference in various ways, all of which stem from ignoring the natural structure of the data. This paper presents a flexible approach to modelling ordered categorical data using the popular R package `mgcv`. An example analysis of …

Hi-d maps: An interactive visualization technique for multi-dimensional categorical data
Radi Muhammad Reza, Benjamin A Watson
https://arxiv.org/abs/2507.07890

Hi-d maps: An interactive visualization technique for multi-dimensional categorical data
In this paper, we present Hi-D maps, a novel method for the visualization of multi-dimensional categorical data. Our work addresses the scarcity of techniques for visualizing a large number of data-dimensions in an effective and space-efficient manner. We have mapped the full data-space onto a 2D regular polygonal region. The polygon is cut hierarchically with lines parallel to a user-controlled, ordered sequence of sides, each representing a dimension. We have used multiple visual cues such as…

Variable selection via knockoffs in missing data settings with categorical predictors
Silvia Bacci, Emanuela Dreassi, Leonardo Grilli, Carla Rampichini
https://arxiv.org/abs/2508.06138

Variable selection via knockoffs in missing data settings with categorical predictors
Large-scale assessment data typically include numerous categorical variables, often affected by missing values. Motivated by the challenges arising in this framework, we extend the knockoffs method for selecting predictors to settings with missing values. Our proposal relies on a preliminary phase consisting of multiple imputations of missing values. Each imputed dataset is then processed using a suitable knockoff filter. We evaluate the performance of the proposed method through a simulation s…

Mutual Influence of Symmetries and Topological Field Theories
Daniel Teixeira, Matthew Yu
https://arxiv.org/abs/2507.06304 https://ar…

Mutual Influence of Symmetries and Topological Field Theories
We study how the fusion 2-category symmetry of a fermionic (2+1)d QFT can be affected when one allows for stacking with TQFTs to be an equivalence relation for QFTs. Focusing on a simple kind of fermionic fusion 2-category described purely by group theoretical data, our results reveal that by allowing for stacking with $\mathrm{Spin}(n)_1$ as an equivalence relation enables a finite set of inequivalent modifications to the original fusion 2-categorical-symmetry. To put our results in a broader …

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
MD Shaikh Rahman, Feiroz Humayara, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
https://arxiv.org/abs/2508.04790

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework sy…

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Zhongyuan Lyu, Yuqi Gu
https://arxiv.org/abs/2506.07167 https://…

Spectral Clustering with Likelihood Refinement is Optimal for Latent Class Recovery
Latent class models are widely used for identifying unobserved subgroups from multivariate categorical data in social sciences, with binary data as a particularly popular example. However, accurately recovering individual latent class memberships and determining the number of classes remains challenging, especially when handling large-scale datasets with many items. This paper proposes a novel two-stage algorithm for latent class models with high-dimensional binary responses. Our method first i…

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
Xin Zhang, Quanyu Zhu, Liangbei Xu, Zain Huda, Wang Zhou, Jin Fang, Dennis van der Staay, Yuxi Hu, Jade Nie, Jiyan Yang, Chunzhi Yang
https://arxiv.org/abs/2508.03854

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of data. In DLRM, the sparse embedding table is a crucial component for managing sparse categorical features. Typically, these tables in industrial DLRMs contain trillions of parameters, necessitating model parallelism strategies to address memory constraints. However, as training systems expand with massive GPUs, the tradi…

Random Utility with Aggregated Alternatives
Yuexin Liao, Kota Saito, Alec Sandroni
https://arxiv.org/abs/2506.00372 https://arxiv.org…

Random Utility with Aggregated Alternatives
This paper studies when discrete choice data involving aggregated alternatives such as categorical data or an outside option can be rationalized by a random utility model (RUM). Aggregation introduces ambiguity in composition: the underlying alternatives may differ across individuals and remain unobserved by the analyst. We characterize the observable implications of RUMs under such ambiguity and show that they are surprisingly weak, implying only monotonicity with respect to adding aggregated …

This https://arxiv.org/abs/2310.01147 has been replaced.
link: https://scholar.google.com/scholar?q=a

Optimizing Symbol Visibility through Displacement
In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper, we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider closed unit square …

Hybrid methods for missing categorical covariates in Cox model
Abdoulaye Dioni, Lynne Moore, Aida Eslami
https://arxiv.org/abs/2507.00151 https://

Hybrid methods for missing categorical covariates in Cox model
Survival analysis aims to explore the relationship between covariates and the time until the occurrence of an event. The Cox proportional hazards model is commonly used for right-censored data, but it is not strictly limited to this type of data. However, the presence of missing values among the covariates, particularly categorical ones, can compromise the validity of the estimates. To address this issue, various classical methods for handling missing data have been proposed within the Cox mode…

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Bla\v{z} \v{S}krlj, Beno\^it Guilleminot, Andra\v{z} Tori
https://arxiv.org/abs/2507.18993

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction, a critical component of modern recommender systems. While these multitask frameworks are widely used in code generation, their application in data-centric research is still largely untapped. This paper presents Agent0, an LLM-driven, agent-based system designed to automate information extraction and feature construction from raw, unstructured text. Categorical f…

Real-World Summarization: When Evaluation Reaches Its Limits
Patr\'icia Schmidtov\'a, Ond\v{r}ej Du\v{s}ek, Saad Mahamood
https://arxiv.org/abs/2507.11508

Real-World Summarization: When Evaluation Reaches Its Limits
We examine evaluation of faithfulness to input data in the context of hotel highlights: brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (Spearman correlation rank of 0.63), often…

An Analytical Neighborhood Enrichment Score for Spatial Omics
Axel Andersson, Hanna Nystr\"om
https://arxiv.org/abs/2506.18692 https://

An Analytical Neighborhood Enrichment Score for Spatial Omics
The neighborhood enrichment test is used to quantify spatial enrichment and depletion between spatial points with categorical labels, which is a common data type in spatial omics. Traditionally, this test relies on a permutation-based Monte Carlo approach, which tends to be computationally expensive for large datasets. In this study, we present a modified version of the test that can be computed analytically. This analytical version showed a minimum Pearson correlation of 0.95 with the conventi…

The Hammock Plot: Where Categorical and Numerical Data Relax Together
Matthias Schonlau, Tiancheng Yang
https://arxiv.org/abs/2506.13630 https://

The Hammock Plot: Where Categorical and Numerical Data Relax Together
Effective methods for visualizing data involving multiple variables, including categorical ones, are limited. The hammock plot (Schonlau, 2003) visualizes both categorical and numerical variables using parallel coordinates. We introduce the Stata implementation hammock. We give numerous examples that explore highlighting, missing values, putting axes on the same scale, and tracing an observation across variables. Further, we introduce parallel univariate plots as an edge case of hammock plots. …

On the analysis of sequential designs without a specified number of observations
Anna Klimova, Tam\'as Rudas
https://arxiv.org/abs/2507.02580 https://

On the analysis of sequential designs without a specified number of observations
The paper focuses on sequential experiments for categorical responses in which whether or not a further observation is made depends on the outcome of a previous experiment. Examples include subsequent medical interventions being performed or not depending on the result of a previous intervention, data about offsprings, life tables, and repeated educational retraininig until a certain proficiency level is achieved. Such experiments do not lead to data with a full Cartesian product structure and,…

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
Maria Let\'icia Salvador, Gabriel Rodrigues Palma, Mariana Coelly Modesto Santos Tavares, Iran Jose Oliveira Silva, Idemauro Antonio Rodrigues de Lara
https://arxiv.org/abs/2507.17004

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
The analysis of longitudinal categorical data can be complex and unfeasible due to the number of parameters involved, characterised by overparameterisation leading to model non-convergence, in addition to problems related to sample size and the presence or absence of overdispersion. In this context, we introduce Bayesian hierarchical models as an alternative methodology to classical statistical techniques for analysing nominal polytomous data in longitudinal studies. The theoretical foundation …

Bayesian Variational Inference for Mixed Data Mixture Models
Junyang Wang, James Bennett, Victor Lhoste, Sarah Filippi
https://arxiv.org/abs/2507.16545 htt…

Bayesian Variational Inference for Mixed Data Mixture Models
Heterogeneous, mixed type datasets including both continuous and categorical variables are ubiquitous, and enriches data analysis by allowing for more complex relationships and interactions to be modelled. Mixture models offer a flexible framework for capturing the underlying heterogeneity and relationships in mixed type datasets. Most current approaches for modelling mixed data either forgo uncertainty quantification and only conduct point estimation, and some use MCMC which incurs a very high…

Topological Sequence Analysis of Genomes: Category Approaches
Jian Liu, Li Shen, Mushal Zia, Guo-Wei Wei
https://arxiv.org/abs/2507.08043 https://

Topological Sequence Analysis of Genomes: Category Approaches
Sequence data, such as DNA, RNA, and protein sequences, exhibit intricate, multi-scale structures that pose significant challenges for conventional analysis methods, particularly those relying on alignment or purely statistical representations. In this work, we introduce category-based topological sequence analysis (CTSA ) of genomes. CTSA models a sequence as a resolution category, capturing its hierarchical structure through a categorical construction. Substructure complexes are then derived …

Fiducial Matching: Differentially Private Inference for Categorical Data
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
https://arxiv.org/abs/2507.11762

Fiducial Matching: Differentially Private Inference for Categorical Data
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases…

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
Zuofu Huang, Yingling Fan, James Hodges, Julian Wolfson
https://arxiv.org/abs/2507.17900

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
The widespread collection of data from mobile and wearable devices has created unprecedented opportunities to study human behavior in fine temporal resolution. One common structure for such data is categorical sequences: ordered, multinomial observations across many time points. These sequences present unique statistical challenges due to their high dimensionality and complex temporal dependence, including both short- and long-term correlations. Yet, there has been relatively little methodologi…